Car_Price_Prediction_using_Machine_Learning_.ipynb

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        "id": "9jLbRxtqkMf9"
      },
      "source": [
        "# Car Price Prediction using Machine Learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "nGAa1xwdkMQP"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
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        "colab": {
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            "text/plain": [
              "                                     name   company  year          Price  \\\n",
              "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007         80,000   \n",
              "1                 Mahindra Jeep CL550 MDI  Mahindra  2006       4,25,000   \n",
              "2              Maruti Suzuki Alto 800 Vxi    Maruti  2018  Ask For Price   \n",
              "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014       3,25,000   \n",
              "4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014       5,75,000   \n",
              "\n",
              "   kms_driven fuel_type  \n",
              "0  45,000 kms    Petrol  \n",
              "1      40 kms    Diesel  \n",
              "2  22,000 kms    Petrol  \n",
              "3  28,000 kms    Petrol  \n",
              "4  36,000 kms    Diesel  "
            ],
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              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>80,000</td>\n",
              "      <td>45,000 kms</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550 MDI</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>4,25,000</td>\n",
              "      <td>40 kms</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Maruti Suzuki Alto 800 Vxi</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>2018</td>\n",
              "      <td>Ask For Price</td>\n",
              "      <td>22,000 kms</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>3,25,000</td>\n",
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              "      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2014</td>\n",
              "      <td>5,75,000</td>\n",
              "      <td>36,000 kms</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "dataset = pd.read_csv(\"quikr_car_dataset.csv\")\n",
        "\n",
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
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      "outputs": [
        {
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            "text/plain": [
              "(892, 6)"
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          "execution_count": 79
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      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['name', 'company', 'year', 'Price', 'kms_driven', 'fuel_type'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
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        "id": "1kuChkDPoEdd",
        "outputId": "dfbc2f85-4cad-4553-944b-915fe20de143"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 892 entries, 0 to 891\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        892 non-null    object\n",
            " 1   company     892 non-null    object\n",
            " 2   year        892 non-null    object\n",
            " 3   Price       892 non-null    object\n",
            " 4   kms_driven  840 non-null    object\n",
            " 5   fuel_type   837 non-null    object\n",
            "dtypes: object(6)\n",
            "memory usage: 41.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
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          "height": 175
        },
        "id": "fkokaRKVoG8f",
        "outputId": "2bf803b6-3d28-4241-8fa9-4f68e6d840fa"
      },
      "execution_count": 82,
      "outputs": [
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            "text/plain": [
              "              name company  year          Price  kms_driven fuel_type\n",
              "count          892     892   892            892         840       837\n",
              "unique         525      48    61            274         258         3\n",
              "top     Honda City  Maruti  2015  Ask For Price  45,000 kms    Petrol\n",
              "freq            13     235   117             35          30       440"
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-57efd247-c760-4208-9173-2c7fb28ee265 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57efd247-c760-4208-9173-2c7fb28ee265');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['year'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLc5eMKjo3e6",
        "outputId": "24c92fa9-4d27-405f-f8f1-194d603ddee4"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2007', '2006', '2018', '2014', '2015', '2012', '2013', '2016',\n",
              "       '2010', '2017', '2008', '2011', '2019', '2009', '2005', '2000',\n",
              "       '...', '150k', 'TOUR', '2003', 'r 15', '2004', 'Zest', '/-Rs',\n",
              "       'sale', '1995', 'ara)', '2002', 'SELL', '2001', 'tion', 'odel',\n",
              "       '2 bs', 'arry', 'Eon', 'o...', 'ture', 'emi', 'car', 'able', 'no.',\n",
              "       'd...', 'SALE', 'digo', 'sell', 'd Ex', 'n...', 'e...', 'D...',\n",
              "       ', Ac', 'go .', 'k...', 'o c4', 'zire', 'cent', 'Sumo', 'cab',\n",
              "       't xe', 'EV2', 'r...', 'zest'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['fuel_type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5TcVjbR7pCZy",
        "outputId": "d492690b-f039-4728-cf39-21d625d96a83"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Petrol', 'Diesel', nan, 'LPG'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['company'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrHs7-FNpOK5",
        "outputId": "1fc33dd2-e5f0-4ea4-9444-3c55a232ed09"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hyundai', 'Mahindra', 'Maruti', 'Ford', 'Skoda', 'Audi', 'Toyota',\n",
              "       'Renault', 'Honda', 'Datsun', 'Mitsubishi', 'Tata', 'Volkswagen',\n",
              "       'I', 'Chevrolet', 'Mini', 'BMW', 'Nissan', 'Hindustan', 'Fiat',\n",
              "       'Commercial', 'MARUTI', 'Force', 'Mercedes', 'Land', 'Yamaha',\n",
              "       'selling', 'URJENT', 'Swift', 'Used', 'Jaguar', 'Jeep', 'tata',\n",
              "       'Sale', 'very', 'Volvo', 'i', '2012', 'Well', 'all', '7', '9',\n",
              "       'scratch', 'urgent', 'sell', 'TATA', 'Any', 'Tara'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cleaning the dataset"
      ],
      "metadata": {
        "id": "VLASvY-Opqdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset.copy()"
      ],
      "metadata": {
        "id": "E1hyI4GupQ1i"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# removing non numeric year from year column\n",
        "\n",
        "dataset = dataset[dataset['year'].str.isnumeric()]"
      ],
      "metadata": {
        "id": "TcNXsYPgpwM3"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# changing the year column to integer\n",
        "\n",
        "dataset['year'] = dataset['year'].astype(int)"
      ],
      "metadata": {
        "id": "kaadtS2qp4vB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d0d3b61-8d9c-48b1-b366-4c3f2d78396d"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-88-ffcc15c92b38>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  dataset['year'] = dataset['year'].astype(int)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vI6zvdhhqONo",
        "outputId": "305b7eae-14d3-4aa7-a94f-278a074c109d"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 842 entries, 0 to 891\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        842 non-null    object\n",
            " 1   company     842 non-null    object\n",
            " 2   year        842 non-null    int64 \n",
            " 3   Price       842 non-null    object\n",
            " 4   kms_driven  840 non-null    object\n",
            " 5   fuel_type   837 non-null    object\n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 46.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HofqTjaqnHy",
        "outputId": "dfa0a65b-9942-4895-9c17-acc76014c1a6"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(842, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# counting the 'ask for price' columns\n",
        "\n",
        "dataset[dataset['Price'] == 'Ask For Price'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19FsBBFsqhZp",
        "outputId": "92d5215e-46f4-4262-8e23-08c507f18249"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name          23\n",
              "company       23\n",
              "year          23\n",
              "Price         23\n",
              "kms_driven    21\n",
              "fuel_type     21\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing ask for price from price column\n",
        "\n",
        "dataset = dataset[dataset['Price'] != 'Ask For Price']"
      ],
      "metadata": {
        "id": "JUSBfNVDqRKl"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXXlTZ6Pqtw9",
        "outputId": "5379f466-51f4-47d7-8722-5f76caf31b50"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(819, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing commas from price column\n",
        "\n",
        "dataset['Price'] = dataset['Price'].str.replace(',', '').astype(int)"
      ],
      "metadata": {
        "id": "GmkCfaUIquLz"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7CYkxVw7cyF",
        "outputId": "8c1f71db-3d85-45b6-a98b-7f428836e504"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 819 entries, 0 to 891\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        819 non-null    object\n",
            " 1   company     819 non-null    object\n",
            " 2   year        819 non-null    int64 \n",
            " 3   Price       819 non-null    int64 \n",
            " 4   kms_driven  819 non-null    object\n",
            " 5   fuel_type   816 non-null    object\n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 44.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fcv3BYhQq6-M",
        "outputId": "9fa1a8d9-1cdc-4d89-ff92-1f4352fe23b0"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     name   company  year   Price  kms_driven  \\\n",
              "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007   80000  45,000 kms   \n",
              "1                 Mahindra Jeep CL550 MDI  Mahindra  2006  425000      40 kms   \n",
              "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014  325000  28,000 kms   \n",
              "4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014  575000  36,000 kms   \n",
              "6                               Ford Figo      Ford  2012  175000  41,000 kms   \n",
              "\n",
              "  fuel_type  \n",
              "0    Petrol  \n",
              "1    Diesel  \n",
              "3    Petrol  \n",
              "4    Diesel  \n",
              "6    Diesel  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-3d518551-9022-40a4-99fd-6aa6c235a1d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>80000</td>\n",
              "      <td>45,000 kms</td>\n",
              "      <td>Petrol</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550 MDI</td>\n",
              "      <td>Mahindra</td>\n",
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              "      <td>425000</td>\n",
              "      <td>40 kms</td>\n",
              "      <td>Diesel</td>\n",
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              "    <tr>\n",
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              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
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              "      <td>575000</td>\n",
              "      <td>36,000 kms</td>\n",
              "      <td>Diesel</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ford Figo</td>\n",
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              "      <td>41,000 kms</td>\n",
              "      <td>Diesel</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "      async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3d518551-9022-40a4-99fd-6aa6c235a1d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d518551-9022-40a4-99fd-6aa6c235a1d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing , and kms and spaces\n",
        "\n",
        "dataset['kms_driven'] = dataset['kms_driven'].str.split(' ').str.get(0).str.replace(',', '')\n",
        "dataset['kms_driven'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZTNsU_mrEen",
        "outputId": "12a80296-d449-4c38-eeb0-66ddd90a04bc"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['45000', '40', '28000', '36000', '41000', '25000', '24530',\n",
              "       '60000', '30000', '32000', '48660', '4000', '16934', '43000',\n",
              "       '35550', '39522', '39000', '55000', '72000', '15975', '70000',\n",
              "       '23452', '35522', '48508', '15487', '82000', '20000', '68000',\n",
              "       '38000', '27000', '33000', '46000', '16000', '47000', '35000',\n",
              "       '30874', '15000', '29685', '130000', '19000', '54000', '13000',\n",
              "       '38200', '22000', '50000', '13500', '3600', '45863', '60500',\n",
              "       '12500', '18000', '13349', '29000', '44000', '42000', '14000',\n",
              "       '49000', '36200', '51000', '104000', '33333', '33600', '5600',\n",
              "       '7500', '26000', '24330', '65480', '200000', '59000', '99000',\n",
              "       '2800', '21000', '11000', '66000', '3000', '7000', '38500',\n",
              "       '37200', '43200', '24800', '45872', '40000', '11400', '97200',\n",
              "       '52000', '31000', '175430', '37000', '65000', '3350', '75000',\n",
              "       '62000', '73000', '2200', '54870', '34580', '97000', '60', '80200',\n",
              "       '3200', '0000', '5000', '588', '71200', '175400', '9300', '56758',\n",
              "       '10000', '56450', '56000', '32700', '9000', '73', '160000',\n",
              "       '58559', '57000', '170000', '80000', '6821', '23000', '34000',\n",
              "       '1800', '400000', '48000', '90000', '12000', '69900', '166000',\n",
              "       '122', '0', '36469', '7800', '24695', '15141', '59910', '100000',\n",
              "       '4500', '129000', '300', '131000', '111111', '59466', '25500',\n",
              "       '44005', '2110', '43222', '100200', '65', '140000', '103553',\n",
              "       '58000', '120000', '49800', '100', '81876', '6020', '55700',\n",
              "       '18500', '53000', '35500', '22134', '1000', '8500', '87000',\n",
              "       '6000', '8000', '55800', '56400', '72160', '11500', '133000',\n",
              "       '2000', '88000', '65422', '117000', '150000', '10750', '6800',\n",
              "       '9800', '57923', '30201', '6200', '37518', '24652', '383', '95000',\n",
              "       '3528', '52500', '47900', '52800', '195000', '48008', '48247',\n",
              "       '9400', '64000', '2137', '10544', '147000', '90001', '48006',\n",
              "       '74000', '85000', '29500', '39700', '67000', '19336', '60105',\n",
              "       '45933', '102563', '28600', '41800', '116000', '42590', '7400',\n",
              "       '54500', '76000', '00', '11523', '38600', '95500', '37458',\n",
              "       '85960', '12516', '30600', '2550', '62500', '69000', '28400',\n",
              "       '68485', '3500', '85455', '63000', '1600', '77000', '26500',\n",
              "       '2875', '13900', '1500', '2450', '1625', '33400', '60123',\n",
              "       '137495', '91200', '146000', '100800', '2100', '2500', '132000',\n",
              "       'Petrol'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[dataset['kms_driven'].str.isnumeric()]"
      ],
      "metadata": {
        "id": "2eUDEo8w6a6U"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElHPE9Lk6wb8",
        "outputId": "2cd52da4-92ea-42ba-c0e9-c68eccd6e7ae"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(817, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['kms_driven'] = dataset['kms_driven'].astype(int)"
      ],
      "metadata": {
        "id": "pmImkbF762zw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99bdf621-d623-4dd1-9e85-df7aaf5c8372"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-100-11edc8e8f43e>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  dataset['kms_driven'] = dataset['kms_driven'].astype(int)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VrkgB4SL6_Se",
        "outputId": "92fa1288-64e2-413f-8ce2-5299d92bc80a"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 817 entries, 0 to 889\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        817 non-null    object\n",
            " 1   company     817 non-null    object\n",
            " 2   year        817 non-null    int64 \n",
            " 3   Price       817 non-null    int64 \n",
            " 4   kms_driven  817 non-null    int64 \n",
            " 5   fuel_type   816 non-null    object\n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 44.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset  = dataset[~dataset['fuel_type'].isna()]"
      ],
      "metadata": {
        "id": "PzEaxd9Y7Nrz"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GKjhHlw8JED",
        "outputId": "ce47773e-4bdb-4d94-debc-1e0d381bd737"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 816 entries, 0 to 889\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        816 non-null    object\n",
            " 1   company     816 non-null    object\n",
            " 2   year        816 non-null    int64 \n",
            " 3   Price       816 non-null    int64 \n",
            " 4   kms_driven  816 non-null    int64 \n",
            " 5   fuel_type   816 non-null    object\n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 44.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset['name'] = dataset['name'].str.split(' ').str.slice(0,3).str.join(' ')\n",
        "dataset['name']=dataset['name'].str.split().str.slice(start=0,stop=3).str.join(' ')"
      ],
      "metadata": {
        "id": "EMYRpwMc8s_b"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset['name'] = dataset['name'].str.split(' ').str.slice(0,3)"
      ],
      "metadata": {
        "id": "3AbAVkpF8-AH"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "id": "ZUd38TLT9HVa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9c2a432b-6da8-4289-c396-b58e5baa6bc2"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     name   company  year   Price  kms_driven fuel_type\n",
              "0     Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
              "1     Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
              "3       Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
              "4  Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
              "6               Ford Figo      Ford  2012  175000       41000    Diesel"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-2f462e7a-3df1-4926-86e3-a608878f79b9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>company</th>\n",
              "      <th>year</th>\n",
              "      <th>Price</th>\n",
              "      <th>kms_driven</th>\n",
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              "    </tr>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>80000</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>425000</td>\n",
              "      <td>40</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hyundai Grand i10</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>325000</td>\n",
              "      <td>28000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ford EcoSport Titanium</td>\n",
              "      <td>Ford</td>\n",
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              "      <td>36000</td>\n",
              "      <td>Diesel</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ford Figo</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2012</td>\n",
              "      <td>175000</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f462e7a-3df1-4926-86e3-a608878f79b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57205be1-c9df-47d4-b033-c0ee637d1894')\"\n",
              "              title=\"Suggest charts.\"\n",
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              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "    domScope.querySelector('#df-57205be1-c9df-47d4-b033-c0ee637d1894 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2f462e7a-3df1-4926-86e3-a608878f79b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2f462e7a-3df1-4926-86e3-a608878f79b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.reset_index(drop = True)"
      ],
      "metadata": {
        "id": "dJMXzO1dN0VC"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "0dECNFB1OQvH",
        "outputId": "3128c210-16ee-4df4-b675-d41b8e123379"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       name   company  year   Price  kms_driven fuel_type\n",
              "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
              "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
              "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
              "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
              "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
              "..                      ...       ...   ...     ...         ...       ...\n",
              "811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
              "812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
              "813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
              "814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
              "815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
              "\n",
              "[816 rows x 6 columns]"
            ],
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              "      <td>Hyundai Santro Xing</td>\n",
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              "      <td>80000</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550</td>\n",
              "      <td>Mahindra</td>\n",
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              "      <td>425000</td>\n",
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              "      <td>325000</td>\n",
              "      <td>28000</td>\n",
              "      <td>Petrol</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ford EcoSport Titanium</td>\n",
              "      <td>Ford</td>\n",
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              "      <td>36000</td>\n",
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              "      <td>Ford Figo</td>\n",
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              "      <td>300000</td>\n",
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              "      <td>Petrol</td>\n",
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              "      <td>Tata Zest XM</td>\n",
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              "      <td>2018</td>\n",
              "      <td>260000</td>\n",
              "      <td>27000</td>\n",
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              "      <th>815</th>\n",
              "      <td>Mahindra Quanto C8</td>\n",
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              "<p>816 rows × 6 columns</p>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-490403fe-b413-4047-80ba-1282258daa1c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset = dataset.drop('Price', axis = 1)"
      ],
      "metadata": {
        "id": "E2dwW75iOS4A"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "AwW3N9utOeYI",
        "outputId": "5268e85e-a4ca-45b6-ad54-0b7c4bf69578"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     name   company  year   Price  kms_driven fuel_type\n",
              "0     Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
              "1     Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
              "2       Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
              "3  Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
              "4               Ford Figo      Ford  2012  175000       41000    Diesel\n",
              "5             Hyundai Eon   Hyundai  2013  190000       25000    Petrol\n",
              "6  Ford EcoSport Ambiente      Ford  2016  830000       24530    Diesel\n",
              "7      Maruti Suzuki Alto    Maruti  2015  250000       60000    Petrol\n",
              "8     Skoda Fabia Classic     Skoda  2010  182000       60000    Petrol\n",
              "9  Maruti Suzuki Stingray    Maruti  2015  315000       30000    Petrol"
            ],
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hyundai Eon</td>\n",
              "      <td>Hyundai</td>\n",
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              "      <td>190000</td>\n",
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              "      <td>Petrol</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ford EcoSport Ambiente</td>\n",
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              "      <th>9</th>\n",
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              "      <td>Maruti</td>\n",
              "      <td>2015</td>\n",
              "      <td>315000</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acc87692-2aa0-4bad-b432-135ec714b17a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-acc87692-2aa0-4bad-b432-135ec714b17a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-acc87692-2aa0-4bad-b432-135ec714b17a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "bN9ruBQPOfWy",
        "outputId": "99c6b1b0-8a6d-40cc-85ef-87cf738c7925"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              year         Price     kms_driven\n",
              "count   816.000000  8.160000e+02     816.000000\n",
              "mean   2012.444853  4.117176e+05   46275.531863\n",
              "std       4.002992  4.751844e+05   34297.428044\n",
              "min    1995.000000  3.000000e+04       0.000000\n",
              "25%    2010.000000  1.750000e+05   27000.000000\n",
              "50%    2013.000000  2.999990e+05   41000.000000\n",
              "75%    2015.000000  4.912500e+05   56818.500000\n",
              "max    2019.000000  8.500003e+06  400000.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-84382a0b-9321-48e3-b9d8-c37993f8b21f\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>Price</th>\n",
              "      <th>kms_driven</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>816.000000</td>\n",
              "      <td>8.160000e+02</td>\n",
              "      <td>816.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2012.444853</td>\n",
              "      <td>4.117176e+05</td>\n",
              "      <td>46275.531863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.002992</td>\n",
              "      <td>4.751844e+05</td>\n",
              "      <td>34297.428044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1995.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1.750000e+05</td>\n",
              "      <td>27000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>2.999990e+05</td>\n",
              "      <td>41000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>4.912500e+05</td>\n",
              "      <td>56818.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>8.500003e+06</td>\n",
              "      <td>400000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84382a0b-9321-48e3-b9d8-c37993f8b21f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    background-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "        displayQuickchartButton(document);\n",
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              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84382a0b-9321-48e3-b9d8-c37993f8b21f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84382a0b-9321-48e3-b9d8-c37993f8b21f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[dataset['Price']<6e6].reset_index(drop = True)"
      ],
      "metadata": {
        "id": "yQkMbEiPOjzu"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "I4R0W-fmO94V",
        "outputId": "e22ce80a-2eeb-4073-fc6e-d1063c3b5ac0"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              year         Price     kms_driven\n",
              "count   815.000000  8.150000e+02     815.000000\n",
              "mean   2012.442945  4.017933e+05   46277.096933\n",
              "std       4.005079  3.815888e+05   34318.459638\n",
              "min    1995.000000  3.000000e+04       0.000000\n",
              "25%    2010.000000  1.750000e+05   27000.000000\n",
              "50%    2013.000000  2.999990e+05   41000.000000\n",
              "75%    2015.000000  4.900000e+05   56879.000000\n",
              "max    2019.000000  3.100000e+06  400000.000000"
            ],
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              "\n",
              "\n",
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              "<style scoped>\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>Price</th>\n",
              "      <th>kms_driven</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>815.000000</td>\n",
              "      <td>8.150000e+02</td>\n",
              "      <td>815.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2012.442945</td>\n",
              "      <td>4.017933e+05</td>\n",
              "      <td>46277.096933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.005079</td>\n",
              "      <td>3.815888e+05</td>\n",
              "      <td>34318.459638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1995.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1.750000e+05</td>\n",
              "      <td>27000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>2.999990e+05</td>\n",
              "      <td>41000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>4.900000e+05</td>\n",
              "      <td>56879.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>3.100000e+06</td>\n",
              "      <td>400000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-094dd0f7-4ef4-47f6-bba1-d81e05f5cc22')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-600c0626-dd62-40fc-99c3-aa0ad9768e30\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-600c0626-dd62-40fc-99c3-aa0ad9768e30')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-600c0626-dd62-40fc-99c3-aa0ad9768e30 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-094dd0f7-4ef4-47f6-bba1-d81e05f5cc22 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-094dd0f7-4ef4-47f6-bba1-d81e05f5cc22');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "gxKJXJU7PAo1",
        "outputId": "fa5bffae-7a86-4bd3-e979-85094e4a5353"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       name   company  year   Price  kms_driven fuel_type\n",
              "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
              "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
              "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
              "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
              "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
              "..                      ...       ...   ...     ...         ...       ...\n",
              "810      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
              "811          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
              "812    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
              "813            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
              "814      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
              "\n",
              "[815 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-a76b065a-3c16-4c07-9d27-889c9d487440\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>company</th>\n",
              "      <th>year</th>\n",
              "      <th>Price</th>\n",
              "      <th>kms_driven</th>\n",
              "      <th>fuel_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>80000</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>425000</td>\n",
              "      <td>40</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Grand i10</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>325000</td>\n",
              "      <td>28000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ford EcoSport Titanium</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2014</td>\n",
              "      <td>575000</td>\n",
              "      <td>36000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ford Figo</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2012</td>\n",
              "      <td>175000</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>810</th>\n",
              "      <td>Maruti Suzuki Ritz</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>2011</td>\n",
              "      <td>270000</td>\n",
              "      <td>50000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>811</th>\n",
              "      <td>Tata Indica V2</td>\n",
              "      <td>Tata</td>\n",
              "      <td>2009</td>\n",
              "      <td>110000</td>\n",
              "      <td>30000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>Toyota Corolla Altis</td>\n",
              "      <td>Toyota</td>\n",
              "      <td>2009</td>\n",
              "      <td>300000</td>\n",
              "      <td>132000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>Tata Zest XM</td>\n",
              "      <td>Tata</td>\n",
              "      <td>2018</td>\n",
              "      <td>260000</td>\n",
              "      <td>27000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>814</th>\n",
              "      <td>Mahindra Quanto C8</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2013</td>\n",
              "      <td>390000</td>\n",
              "      <td>40000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>815 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a76b065a-3c16-4c07-9d27-889c9d487440')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-1de493bb-507f-449e-823d-e3e989f60fc2\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1de493bb-507f-449e-823d-e3e989f60fc2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1de493bb-507f-449e-823d-e3e989f60fc2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a76b065a-3c16-4c07-9d27-889c9d487440 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a76b065a-3c16-4c07-9d27-889c9d487440');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a csv file and saving it\n",
        "\n",
        "dataset.to_csv('cleaned_car_dataset.csv')"
      ],
      "metadata": {
        "id": "BDX7mkjjPVdI"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting Features and Target columns"
      ],
      "metadata": {
        "id": "04bBxgdOQdYG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.drop(columns = 'Price')\n",
        "y = dataset['Price']"
      ],
      "metadata": {
        "id": "KJg0KLiaQZgQ"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "g6iIh0BmRGOZ",
        "outputId": "4cadc8e1-8f5d-4c3f-cc76-1f412a51bcaf"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     name   company  year  kms_driven fuel_type\n",
              "0     Hyundai Santro Xing   Hyundai  2007       45000    Petrol\n",
              "1     Mahindra Jeep CL550  Mahindra  2006          40    Diesel\n",
              "2       Hyundai Grand i10   Hyundai  2014       28000    Petrol\n",
              "3  Ford EcoSport Titanium      Ford  2014       36000    Diesel\n",
              "4               Ford Figo      Ford  2012       41000    Diesel"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-84ce9185-4732-4741-b191-be8c287022c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>company</th>\n",
              "      <th>year</th>\n",
              "      <th>kms_driven</th>\n",
              "      <th>fuel_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>40</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Grand i10</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>28000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ford EcoSport Titanium</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2014</td>\n",
              "      <td>36000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ford Figo</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2012</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84ce9185-4732-4741-b191-be8c287022c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-bd05562b-4639-4458-a33f-ea5e08286fc3\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd05562b-4639-4458-a33f-ea5e08286fc3')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-bd05562b-4639-4458-a33f-ea5e08286fc3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84ce9185-4732-4741-b191-be8c287022c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84ce9185-4732-4741-b191-be8c287022c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-iqiTuGRHT8",
        "outputId": "779f0de8-5b08-44d8-84ed-640ab5e44272"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     80000\n",
              "1    425000\n",
              "2    325000\n",
              "3    575000\n",
              "4    175000\n",
              "Name: Price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "syzlnJ-fRIWW"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "from sklearn.compose  import make_column_transformer\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "metadata": {
        "id": "apxLx9aCRoZK"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ohe = OneHotEncoder()\n",
        "ohe.fit(X[['name', 'company', 'fuel_type']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "YDpB_jdFRzEj",
        "outputId": "5891ea5c-8f2f-4a60-f371-f29a5a88885b"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe.categories_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUzu5DeRt7fn",
        "outputId": "074b2f6e-b8d8-44ee-c4d9-36a63d546f4a"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
              "        'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
              "        'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
              "        'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n",
              "        'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n",
              "        'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n",
              "        'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n",
              "        'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n",
              "        'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n",
              "        'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n",
              "        'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n",
              "        'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n",
              "        'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n",
              "        'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n",
              "        'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n",
              "        'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n",
              "        'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n",
              "        'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n",
              "        'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n",
              "        'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n",
              "        'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n",
              "        'Hyundai Accent Executive', 'Hyundai Accent GLE',\n",
              "        'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n",
              "        'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n",
              "        'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n",
              "        'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n",
              "        'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n",
              "        'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n",
              "        'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n",
              "        'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n",
              "        'Hyundai Verna Transform', 'Hyundai Verna VGT',\n",
              "        'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n",
              "        'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n",
              "        'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n",
              "        'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n",
              "        'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n",
              "        'Land Rover Freelander', 'Mahindra Bolero DI',\n",
              "        'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n",
              "        'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n",
              "        'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n",
              "        'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n",
              "        'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n",
              "        'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n",
              "        'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n",
              "        'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n",
              "        'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n",
              "        'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n",
              "        'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n",
              "        'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n",
              "        'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n",
              "        'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n",
              "        'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n",
              "        'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n",
              "        'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n",
              "        'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n",
              "        'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n",
              "        'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n",
              "        'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n",
              "        'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n",
              "        'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n",
              "        'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n",
              "        'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n",
              "        'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n",
              "        'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n",
              "        'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n",
              "        'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n",
              "        'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n",
              "        'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n",
              "        'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n",
              "        'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n",
              "        'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n",
              "        'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n",
              "        'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n",
              "        'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n",
              "        'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n",
              "        'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n",
              "        'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n",
              "        'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n",
              "        'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n",
              "        'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n",
              "        'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n",
              "        'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n",
              "        'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n",
              "        'Volkswagen Passat Diesel', 'Volkswagen Polo',\n",
              "        'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n",
              "        'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n",
              "        'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n",
              "        'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object),\n",
              " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
              "        'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
              "        'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
              "        'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
              "       dtype=object),\n",
              " array(['Diesel', 'LPG', 'Petrol'], dtype=object)]"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_trans = make_column_transformer((OneHotEncoder(categories = ohe.categories_), ['name', 'company', 'fuel_type']), remainder = 'passthrough')"
      ],
      "metadata": {
        "id": "mZ15YNhmSFmy"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()"
      ],
      "metadata": {
        "id": "_nPTLIVutQO-"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = make_pipeline(column_trans, lr)"
      ],
      "metadata": {
        "id": "6dClsQQvtTmp"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "iGEgbAmvtXa6",
        "outputId": "a888efb2-0ea0-4e2b-a75f-e4d08c66bdd7"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
              "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
              "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
              "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n",
              "                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
              "       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
              "       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
              "       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
              "      dtype=object),\n",
              "                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n",
              "                                                  ['name', 'company',\n",
              "                                                   'fuel_type'])])),\n",
              "                ('linearregression', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n",
              "                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
              "      dtype=object),\n",
              "                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n",
              "                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n",
              "                                                   &#x27;fuel_type&#x27;])])),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n",
              "                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
              "      dtype=object),\n",
              "                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n",
              "                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n",
              "                                                   &#x27;fuel_type&#x27;])])),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                 OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n",
              "       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet B...\n",
              "       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n",
              "                                                           array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
              "      dtype=object),\n",
              "                                                           array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n",
              "                                 [&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n",
              "       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet Beat LT&#x27;, &#x27;Chevrolet Beat PS&#x27;,\n",
              "       &#x27;Chevrolet Cruze LTZ&#x27;, &#x27;Chevrolet Enjoy&#x27;, &#x27;Chevrolet E...\n",
              "       &#x27;Volkswagen Vento Comfortline&#x27;, &#x27;Volkswagen Vento Highline&#x27;,\n",
              "       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n",
              "                          array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
              "      dtype=object),\n",
              "                          array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year&#x27;, &#x27;kms_driven&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = pipe.predict(X_test)"
      ],
      "metadata": {
        "id": "L-FjJyvAtaOj"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dofxPZeMucio",
        "outputId": "39f55706-a38d-48d2-e6b3-f97de8e86a90"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7239646424103914"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores=[]\n",
        "\n",
        "for i in range(1000):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= i)\n",
        "  lr = LinearRegression()\n",
        "  pipe = make_pipeline(column_trans, lr)\n",
        "  pipe.fit(X_train, y_train)\n",
        "  y_pred = pipe.predict(X_test)\n",
        "  print(r2_score(y_test, y_pred), i)\n",
        "  scores.append(r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTNJQabYuhEe",
        "outputId": "4f6accc0-d106-4799-874b-4ce03fc1d164"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "0.4977884982638734 916\n",
            "0.5032264862309206 917\n",
            "0.7337656561834882 918\n",
            "0.46689059663702903 919\n",
            "0.5122938702446244 920\n",
            "0.6597835208421803 921\n",
            "0.7814092431655587 922\n",
            "0.6790835379023373 923\n",
            "0.5812663604628334 924\n",
            "0.569735233178295 925\n",
            "0.7859800911787939 926\n",
            "0.6176284183580805 927\n",
            "0.6737491934053115 928\n",
            "0.7424209841685012 929\n",
            "0.5260106550579875 930\n",
            "0.7026287035616949 931\n",
            "0.4956976660150244 932\n",
            "0.6944619801094625 933\n",
            "0.6326887714374114 934\n",
            "0.5528013388106777 935\n",
            "0.6196276087838727 936\n",
            "0.5617965472467557 937\n",
            "0.7529616075342109 938\n",
            "0.5118450479803238 939\n",
            "0.5481084394794367 940\n",
            "0.5605046664618654 941\n",
            "0.6917652541361246 942\n",
            "0.6340932833382557 943\n",
            "0.6702872821844037 944\n",
            "0.7404623854589474 945\n",
            "0.5021774456075894 946\n",
            "0.5742311221222626 947\n",
            "0.6728602829634847 948\n",
            "0.7069528880683137 949\n",
            "0.4986922706360222 950\n",
            "0.6103423052322587 951\n",
            "0.7186991489825056 952\n",
            "0.6509514608696709 953\n",
            "0.650277219192656 954\n",
            "0.6382858136929619 955\n",
            "0.574873900385205 956\n",
            "0.6891853473797864 957\n",
            "0.7221457054218989 958\n",
            "0.6466806536446704 959\n",
            "0.4483326255768786 960\n",
            "0.6300539284208191 961\n",
            "0.6460810147686435 962\n",
            "0.5201682198403148 963\n",
            "0.5641256368891869 964\n",
            "0.5897661908032421 965\n",
            "0.7199747874690098 966\n",
            "0.7809856901535315 967\n",
            "0.6670979829894096 968\n",
            "0.7286900640082563 969\n",
            "0.4828514688925274 970\n",
            "0.5573403190619627 971\n",
            "0.690886216738765 972\n",
            "0.5118422255682438 973\n",
            "0.758181796035433 974\n",
            "0.5858853800073076 975\n",
            "0.5847821401560215 976\n",
            "0.7392674843654712 977\n",
            "0.5710650040825596 978\n",
            "0.4342604717238636 979\n",
            "0.5797150143488021 980\n",
            "0.7648849875892488 981\n",
            "0.6532514997811639 982\n",
            "0.6935344745275038 983\n",
            "0.5358040185690442 984\n",
            "0.5637579548423186 985\n",
            "0.7718161036448886 986\n",
            "0.5998520088570215 987\n",
            "0.6466732680590133 988\n",
            "0.779106718636753 989\n",
            "0.5781226046085159 990\n",
            "0.5177989801832026 991\n",
            "0.6969855497930588 992\n",
            "0.6501644339034827 993\n",
            "0.6533766042800565 994\n",
            "0.6284977497887146 995\n",
            "0.7158210710327527 996\n",
            "0.676754430911594 997\n",
            "0.6034259228092894 998\n",
            "0.5926370421018297 999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4sYzjm9u9hR",
        "outputId": "6e8c6d42-2275-4ad5-cad5-4b1839b0efff"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "433"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores[np.argmax(scores)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RS6hbL3vjCV",
        "outputId": "4bd76df7-c6c0-41fe-f381-1a02953e6951"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8456515104452564"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= np.argmax(scores))\n",
        "lr = LinearRegression()\n",
        "pipe = make_pipeline(column_trans, lr)\n",
        "pipe.fit(X_train, y_train)\n",
        "y_pred = pipe.predict(X_test)\n",
        "print(r2_score(y_test, y_pred))\n",
        "scores.append(r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13D61k3YvpMf",
        "outputId": "983b5643-5e18-46fa-c8c6-1828ff476afe"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8456515104452564\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the model"
      ],
      "metadata": {
        "id": "PWqBCAy6wELv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "nwLAsP2PwCMF"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(pipe, open('LinearRegressionModel.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "HnNapVc7wHPS"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prediction"
      ],
      "metadata": {
        "id": "wkWI6MFvweik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.predict(pd.DataFrame([['Maruti Suzuki Swift','Maruti', 2019, 100, 'Petrol']], columns = ['name', 'company', 'year', 'kms_driven', 'fuel_type']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-p9aTMqywMfo",
        "outputId": "aa4c209f-4932-4113-e535-b0a290881fa4"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([459113.49353657])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ofhpr3uvxYo6"
      },
      "execution_count": 153,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}